%0 Journal Article %A Ryan M. Mulqueen %A Dmitry Pokholok %A Steve Norberg %A Andrew J. Fields %A Duanchen Sun %A Kristof A. Torkenczy %A Jay Shendure %A Cole Trapnell %A Brian J. O’Roak %A Zheng Xia %A Frank J. Steemers %A Andrew C. Adey %T Scalable and efficient single-cell DNA methylation sequencing by combinatorial indexing %D 2017 %R 10.1101/157230 %J bioRxiv %P 157230 %X Here we present a novel method: single-cell combinatorial indexing for methylation analysis (sci-MET), which is the first highly scalable assay for whole genome methylation profiling of single cells. We use sci-MET to produce 2,697 total single-cell bisulfite sequencing libraries and achieve read alignment rates of 69 ± 7%, comparable to those of bulk cell methods. As a proof of concept, we applied sci-MET to successfully deconvolve the cellular identity of a mixture of three human cell lines. %U https://www.biorxiv.org/content/biorxiv/early/2017/06/28/157230.full.pdf